Department of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Hangzhou, China.
Front Endocrinol (Lausanne). 2024 Mar 14;15:1292458. doi: 10.3389/fendo.2024.1292458. eCollection 2024.
Preeclampsia (PE) is one of the most severe pregnancy-related diseases; however, there is still a lack of reliable biomarkers. In this study, we aimed to develop models for predicting early-onset PE, severe PE, and the gestation duration of patients with PE.
Eligible patients with PE were enrolled and divided into a training ( = 253) and a validation ( = 108) cohort. Multivariate logistic and Cox models were used to identify factors associated with early-onset PE, severe PE, and the gestation duration of patients with PE. Based on significant factors, nomograms were developed and evaluated using the area under the curve (AUC) and a calibration curve.
In the training cohort, multiple gravidity experience ( = 0.005), lower albumin (ALB; < 0.001), and higher lactate dehydrogenase (LDH; < 0.001) were significantly associated with early-onset PE. Abortion history ( = 0.017), prolonged thrombin time (TT; < 0.001), and higher aspartate aminotransferase ( = 0.002) and LDH ( = 0.003) were significantly associated with severe PE. Abortion history ( < 0.001), gemellary pregnancy ( < 0.001), prolonged TT ( < 0.001), higher mean platelet volume ( = 0.014) and LDH ( < 0.001), and lower ALB ( < 0.001) were significantly associated with shorter gestation duration. Three nomograms were developed and validated to predict the probability of early-onset PE, severe PE, and delivery time for each patient with PE. The AUC showed good predictive performance, and the calibration curve and decision curve analysis demonstrated clinical practicability.
Based on the clinical features and peripheral blood laboratory indicators, we identified significant factors and developed models to predict early-onset PE, severe PE, and the gestation duration of pregnant women with PE, which could help clinicians assess the clinical outcomes early and design appropriate strategies for patients.
子痫前期（PE）是最严重的妊娠相关疾病之一；然而，仍然缺乏可靠的生物标志物。本研究旨在建立预测早发型 PE、重度 PE 及 PE 患者妊娠时间的模型。
纳入符合条件的 PE 患者，并将其分为训练队列（n=253）和验证队列（n=108）。使用多变量逻辑回归和 Cox 模型确定与早发型 PE、重度 PE 及 PE 患者妊娠时间相关的因素。基于显著因素，建立并通过曲线下面积（AUC）和校准曲线评估列线图。
在训练队列中，多胎妊娠史（P=0.005）、低白蛋白（ALB；P<0.001）和高乳酸脱氢酶（LDH；P<0.001）与早发型 PE 显著相关。流产史（P=0.017）、延长的凝血酶时间（TT；P<0.001）和较高的天门冬氨酸氨基转移酶（AST；P=0.002）和 LDH（P=0.003）与重度 PE 显著相关。流产史（P<0.001）、双胎妊娠（P<0.001）、延长的 TT（P<0.001）、较高的平均血小板体积（P=0.014）和 LDH（P<0.001）以及低 ALB（P<0.001）与较短的妊娠时间显著相关。建立并验证了三个列线图来预测每位 PE 患者发生早发型 PE、重度 PE 和分娩的概率。AUC 显示了良好的预测性能，校准曲线和决策曲线分析显示了临床实用性。
基于临床特征和外周血实验室指标，我们确定了显著因素并建立了预测 PE 患者早发型 PE、重度 PE 和妊娠时间的模型，这有助于临床医生早期评估临床结局并为患者制定适当的策略。
